Moving Window Approaches for Hedonic Price Estimation

An Empirical Comparison of Modelling Techniques

Dados Bibliográficos

ID10859017
AutoresAntonio Páez (0000-0001-6912-9919, McMaster University, autor correspondente), Fei Long Fei Long (North American Development Group, 2851 John Street, Suite 1, Markham, Ontario, Canada, L3R5R7), Fei Long (0000-0002-8906-833X), Farber (0000-0002-3870-5984, McMaster University), Steven Farber (0000-0002-8037-7312, McMaster University)
Ano2008
Volume45
Fascículo8
Páginas1565-1581
Data de publicação2008-07-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoUrban Studies (JOURNAL)
Identificadores do periódicoISSN: 0042-0980 • E-ISSN: 1360-063X
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/0042098008091491
OpenAlexW2156378058
IdiomaEN
Citações recebidas30
Referências citadas38

Recognition of the limitations of traditional hedonic models to account for spatial effects has led in recent years to the development and use of spatial econometric and statistical techniques in real estate applications. It seems appropriate, as the number of applications grows, to evaluate the relative ability of some newer approaches in terms of producing accurate spatial predictions. This article compares a selection of techniques to assess their performance. The focus is on moving window approaches that can be conceptualised as sliding neighbourhoods (i.e. soft market segmentations) and that can incorporate spatial dependency effects. Comparison of moving windows regression (MWR), geographically weighted regression (GWR) and moving windows Kriging (MWK) sheds light on the relevance of different spatial effects. Results using Toronto as a case study indicate that market segmentation may be more important than spatial dependencies. The findings suggest practical guidelines with regard to the use of the models investigated

Data mining · Econometrics · Economics · Estimation · Focus (optics · Hedonic Pricing · Hedonic regression · Kriging · Machine learning · Market segmentation · Real estate · Regression · Relevance (law · Segmentation · Statistics · Window (computing · Artificial Intelligence · Computer Science · Economic and Environmental Valuation · Housing Market and Economics · Mathematics · Spatial and Panel Data Analysis